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<main>
<article id="content">
<header>
<h1 class="title">Module <code>silk.utils.jax</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python"># Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

# source : https://github.com/lucidrains/jax2torch/blob/main/jax2torch/jax2torch.py
# modified `vjp` use for 2x speed-up on large inputs

import os
from functools import wraps
from inspect import signature

import jax
import jax.numpy as jnp
import torch
from jax import dlpack as jax_dlpack, tree_flatten, tree_unflatten
from jax.tree_util import tree_map
from torch.utils import dlpack as torch_dlpack

# To avoid having jax taking all the VRAM
# https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
os.environ[&#34;XLA_PYTHON_CLIENT_PREALLOCATE&#34;] = &#34;false&#34;


def j2t(x_jax, device=None):
    x_torch = torch_dlpack.from_dlpack(jax_dlpack.to_dlpack(x_jax))
    if device:
        x_torch = x_torch.to(device)
    return x_torch


def t2j(x_torch, device=None):
    x_torch = x_torch.contiguous()  # https://github.com/google/jax/issues/8082
    if device:
        x_torch = x_torch.to(device)
    x_jax = jax_dlpack.from_dlpack(torch_dlpack.to_dlpack(x_torch))
    return x_jax


def tree_t2j(x_torch, device=None):
    return tree_map(
        lambda t: t2j(t, device=device) if isinstance(t, torch.Tensor) else t,
        x_torch,
    )


def tree_j2t(x_jax, device=None):
    return tree_map(
        lambda t: j2t(t, device=device) if isinstance(t, jnp.ndarray) else t,
        x_jax,
    )


def tree_get_devices(x_torch):
    return tree_map(
        lambda t: t.device if isinstance(t, torch.Tensor) else None, x_torch
    )


def find_unique_device(devices):
    device = None
    flat_d, _ = tree_flatten(devices)
    for d in flat_d:
        if d:
            if device is None:
                device = d
            else:
                if d != device:
                    raise RuntimeError(
                        f&#34;there should be a unique device in the pytree, found {device} and {d}&#34;
                    )
    return device


def tree_set_devices(x_torch, devices):
    flat_x, tree_x = tree_flatten(x_torch)
    flat_d, tree_d = tree_flatten(devices)

    assert tree_d == tree_x

    flat_r = [
        x.to(d) if isinstance(x, torch.Tensor) else x for x, d in zip(flat_x, flat_d)
    ]

    return tree_unflatten(tree_x, flat_r)


def jax2torch(fn, backward_pass=True):
    class JaxFun(torch.autograd.Function):
        @staticmethod
        def forward(ctx, *args):
            args, jax_device, torch_device = args[:-2], args[-2], args[-1]

            if torch_device is None:
                torch_device = find_unique_device(tree_get_devices(args))

            args = tree_t2j(args, device=jax_device)

            if backward_pass:
                y_, ctx.fun_vjp = jax.vjp(fn, *args)
                ctx.jax_device = jax_device
            else:
                y_ = fn(*args)

            return tree_j2t(y_, device=torch_device)

        @staticmethod
        def backward(ctx, *grad_args):
            if not backward_pass:
                return (None,) * (len(grad_args) + 2)

            device = find_unique_device(tree_get_devices(grad_args))
            grad_args = (
                tree_t2j(grad_args, device=ctx.jax_device)
                if len(grad_args) &gt; 1
                else t2j(grad_args[0])
            )

            grads = ctx.fun_vjp(grad_args)

            grads = tuple(
                map(lambda t: t if isinstance(t, jnp.ndarray) else None, grads)
            ) + (None, None)

            return tree_j2t(grads, device=device)

    @wraps(fn)
    def inner(*args, jax_device=None, torch_device=None, **kwargs):
        sig = signature(fn)
        bound = sig.bind(*args, **kwargs)
        bound.apply_defaults()

        return JaxFun.apply(
            *(tuple(bound.arguments.values()) + (jax_device, torch_device))
        )

    return inner


def delayed_vjp(fun):
    @jax.custom_vjp
    def new_fun(*args):
        return fun(*args)

    def fun_fwd(*args):
        return new_fun(*args), args

    def fun_bwd(args, g):
        _, vjp = jax.vjp(fun, *args)
        return vjp(g)

    new_fun.defvjp(fun_fwd, fun_bwd)

    return new_fun</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="silk.utils.jax.delayed_vjp"><code class="name flex">
<span>def <span class="ident">delayed_vjp</span></span>(<span>fun)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def delayed_vjp(fun):
    @jax.custom_vjp
    def new_fun(*args):
        return fun(*args)

    def fun_fwd(*args):
        return new_fun(*args), args

    def fun_bwd(args, g):
        _, vjp = jax.vjp(fun, *args)
        return vjp(g)

    new_fun.defvjp(fun_fwd, fun_bwd)

    return new_fun</code></pre>
</details>
</dd>
<dt id="silk.utils.jax.find_unique_device"><code class="name flex">
<span>def <span class="ident">find_unique_device</span></span>(<span>devices)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def find_unique_device(devices):
    device = None
    flat_d, _ = tree_flatten(devices)
    for d in flat_d:
        if d:
            if device is None:
                device = d
            else:
                if d != device:
                    raise RuntimeError(
                        f&#34;there should be a unique device in the pytree, found {device} and {d}&#34;
                    )
    return device</code></pre>
</details>
</dd>
<dt id="silk.utils.jax.j2t"><code class="name flex">
<span>def <span class="ident">j2t</span></span>(<span>x_jax, device=None)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def j2t(x_jax, device=None):
    x_torch = torch_dlpack.from_dlpack(jax_dlpack.to_dlpack(x_jax))
    if device:
        x_torch = x_torch.to(device)
    return x_torch</code></pre>
</details>
</dd>
<dt id="silk.utils.jax.jax2torch"><code class="name flex">
<span>def <span class="ident">jax2torch</span></span>(<span>fn, backward_pass=True)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def jax2torch(fn, backward_pass=True):
    class JaxFun(torch.autograd.Function):
        @staticmethod
        def forward(ctx, *args):
            args, jax_device, torch_device = args[:-2], args[-2], args[-1]

            if torch_device is None:
                torch_device = find_unique_device(tree_get_devices(args))

            args = tree_t2j(args, device=jax_device)

            if backward_pass:
                y_, ctx.fun_vjp = jax.vjp(fn, *args)
                ctx.jax_device = jax_device
            else:
                y_ = fn(*args)

            return tree_j2t(y_, device=torch_device)

        @staticmethod
        def backward(ctx, *grad_args):
            if not backward_pass:
                return (None,) * (len(grad_args) + 2)

            device = find_unique_device(tree_get_devices(grad_args))
            grad_args = (
                tree_t2j(grad_args, device=ctx.jax_device)
                if len(grad_args) &gt; 1
                else t2j(grad_args[0])
            )

            grads = ctx.fun_vjp(grad_args)

            grads = tuple(
                map(lambda t: t if isinstance(t, jnp.ndarray) else None, grads)
            ) + (None, None)

            return tree_j2t(grads, device=device)

    @wraps(fn)
    def inner(*args, jax_device=None, torch_device=None, **kwargs):
        sig = signature(fn)
        bound = sig.bind(*args, **kwargs)
        bound.apply_defaults()

        return JaxFun.apply(
            *(tuple(bound.arguments.values()) + (jax_device, torch_device))
        )

    return inner</code></pre>
</details>
</dd>
<dt id="silk.utils.jax.t2j"><code class="name flex">
<span>def <span class="ident">t2j</span></span>(<span>x_torch, device=None)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def t2j(x_torch, device=None):
    x_torch = x_torch.contiguous()  # https://github.com/google/jax/issues/8082
    if device:
        x_torch = x_torch.to(device)
    x_jax = jax_dlpack.from_dlpack(torch_dlpack.to_dlpack(x_torch))
    return x_jax</code></pre>
</details>
</dd>
<dt id="silk.utils.jax.tree_get_devices"><code class="name flex">
<span>def <span class="ident">tree_get_devices</span></span>(<span>x_torch)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def tree_get_devices(x_torch):
    return tree_map(
        lambda t: t.device if isinstance(t, torch.Tensor) else None, x_torch
    )</code></pre>
</details>
</dd>
<dt id="silk.utils.jax.tree_j2t"><code class="name flex">
<span>def <span class="ident">tree_j2t</span></span>(<span>x_jax, device=None)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def tree_j2t(x_jax, device=None):
    return tree_map(
        lambda t: j2t(t, device=device) if isinstance(t, jnp.ndarray) else t,
        x_jax,
    )</code></pre>
</details>
</dd>
<dt id="silk.utils.jax.tree_set_devices"><code class="name flex">
<span>def <span class="ident">tree_set_devices</span></span>(<span>x_torch, devices)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def tree_set_devices(x_torch, devices):
    flat_x, tree_x = tree_flatten(x_torch)
    flat_d, tree_d = tree_flatten(devices)

    assert tree_d == tree_x

    flat_r = [
        x.to(d) if isinstance(x, torch.Tensor) else x for x, d in zip(flat_x, flat_d)
    ]

    return tree_unflatten(tree_x, flat_r)</code></pre>
</details>
</dd>
<dt id="silk.utils.jax.tree_t2j"><code class="name flex">
<span>def <span class="ident">tree_t2j</span></span>(<span>x_torch, device=None)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def tree_t2j(x_torch, device=None):
    return tree_map(
        lambda t: t2j(t, device=device) if isinstance(t, torch.Tensor) else t,
        x_torch,
    )</code></pre>
</details>
</dd>
</dl>
</section>
<section>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="silk.utils" href="index.html">silk.utils</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="two-column">
<li><code><a title="silk.utils.jax.delayed_vjp" href="#silk.utils.jax.delayed_vjp">delayed_vjp</a></code></li>
<li><code><a title="silk.utils.jax.find_unique_device" href="#silk.utils.jax.find_unique_device">find_unique_device</a></code></li>
<li><code><a title="silk.utils.jax.j2t" href="#silk.utils.jax.j2t">j2t</a></code></li>
<li><code><a title="silk.utils.jax.jax2torch" href="#silk.utils.jax.jax2torch">jax2torch</a></code></li>
<li><code><a title="silk.utils.jax.t2j" href="#silk.utils.jax.t2j">t2j</a></code></li>
<li><code><a title="silk.utils.jax.tree_get_devices" href="#silk.utils.jax.tree_get_devices">tree_get_devices</a></code></li>
<li><code><a title="silk.utils.jax.tree_j2t" href="#silk.utils.jax.tree_j2t">tree_j2t</a></code></li>
<li><code><a title="silk.utils.jax.tree_set_devices" href="#silk.utils.jax.tree_set_devices">tree_set_devices</a></code></li>
<li><code><a title="silk.utils.jax.tree_t2j" href="#silk.utils.jax.tree_t2j">tree_t2j</a></code></li>
</ul>
</li>
</ul>
</nav>
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